Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- action_head--10000_checkpoint.pt +3 -0
- bitvla_for_action_prediction.py +459 -0
- config.json +70 -0
- configuration_bit_vla.py +14 -0
- dataset_statistics.json +133 -0
- generation_config.json +6 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +762 -0
- preprocessor_config.json +24 -0
- processor_config.json +7 -0
- proprio_projector--10000_checkpoint.pt +3 -0
- special_tokens_map.json +1783 -0
- tokenizer.json +3 -0
- tokenizer_config.json +2383 -0
.gitattributes
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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action_head--10000_checkpoint.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:6dbf79783458845ba427eb4412ee6cd98aa508a8cb164e8e1b14fded56547238
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size 118125406
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bitvla_for_action_prediction.py
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1 |
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from transformers import LlavaForConditionalGeneration,PretrainedConfig
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from configuration_bit_vla import Bitvla_Config
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import numpy as np
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import torch
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from prismatic.vla.constants import (
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ACTION_DIM,
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ACTION_PROPRIO_NORMALIZATION_TYPE,
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NUM_ACTIONS_CHUNK,
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NormalizationType,
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)
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from typing import Optional, Dict, Any,List,Tuple
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13 |
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from transformers.models.llava.modeling_llava import LlavaCausalLMOutputWithPast
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15 |
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from prismatic.training.train_utils import (
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get_current_action_mask,
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17 |
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get_next_actions_mask,
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18 |
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)
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20 |
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class BitVLAForActionPrediction(LlavaForConditionalGeneration):
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config_class: PretrainedConfig = Bitvla_Config
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+
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def __init__(self, config) -> None:
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super().__init__(config)
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self.norm_stats = config.norm_stats
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27 |
+
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# Compute action bins
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self.bins = np.linspace(-1, 1, config.n_action_bins)
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30 |
+
self.bin_centers = (self.bins[:-1] + self.bins[1:]) / 2.0
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31 |
+
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32 |
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self.vocab_size = self.config.vocab_size
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+
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def set_constant(self, image_token_idx, proprio_pad_idx, ignore_idx, action_token_begin_idx, stop_index):
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self.image_token_idx = image_token_idx
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36 |
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self.proprio_pad_idx = proprio_pad_idx
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37 |
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self.action_token_begin_idx = action_token_begin_idx
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self.stop_index = stop_index
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39 |
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self.ignore_idx = ignore_idx
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40 |
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41 |
+
def forward(
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42 |
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self,
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43 |
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input_ids: Optional[torch.LongTensor] = None,
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44 |
+
position_ids: Optional[torch.LongTensor] = None,
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45 |
+
attention_mask: Optional[torch.Tensor] = None,
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46 |
+
pixel_values: Optional[torch.FloatTensor] = None,
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47 |
+
labels: Optional[torch.LongTensor] = None,
|
48 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
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49 |
+
past_key_values: Optional[List[torch.FloatTensor]] = None,
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50 |
+
use_cache: Optional[bool] = None,
|
51 |
+
output_attentions: Optional[bool] = None,
|
52 |
+
output_hidden_states: Optional[bool] = None,
|
53 |
+
output_projector_features: Optional[bool] = None,
|
54 |
+
return_dict: Optional[bool] = None,
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55 |
+
proprio=None,
|
56 |
+
proprio_projector=None,
|
57 |
+
cache_position: Optional[torch.LongTensor] = None,
|
58 |
+
vision_feature_layer=None,
|
59 |
+
vision_feature_select_strategy=None,
|
60 |
+
) -> Tuple[int, LlavaCausalLMOutputWithPast]:
|
61 |
+
"""Run a forward pass through the VLM, returning a PrismaticCausalLMOutputWithPast instance."""
|
62 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
63 |
+
output_hidden_states = (
|
64 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
65 |
+
)
|
66 |
+
output_projector_features = output_projector_features if output_projector_features is not None else False
|
67 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
68 |
+
|
69 |
+
# Respect `use_cache` only if not training (even if `gradient_checkpointing` is off)
|
70 |
+
use_cache = use_cache and not self.training
|
71 |
+
|
72 |
+
batch_size = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0] # type: ignore
|
73 |
+
|
74 |
+
# === Handle Multimodal Forward ===
|
75 |
+
if (input_ids.shape[0] == pixel_values.shape[0]) or (inputs_embeds.shape[0] == pixel_values.shape[0]):
|
76 |
+
assert past_key_values is None, "Unexpected key `past_key_values` provided during multimodal forward!"
|
77 |
+
|
78 |
+
# Get input embeddings
|
79 |
+
inputs_embeds = self.get_input_embeddings()(input_ids) # (B, seq_len, D)
|
80 |
+
|
81 |
+
# change the vision padding to the real vision tokens
|
82 |
+
if pixel_values is not None:
|
83 |
+
vision_feature_layer = (
|
84 |
+
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
85 |
+
)
|
86 |
+
vision_feature_select_strategy = (
|
87 |
+
vision_feature_select_strategy
|
88 |
+
if vision_feature_select_strategy is not None
|
89 |
+
else self.config.vision_feature_select_strategy
|
90 |
+
)
|
91 |
+
# pixel_values: b,num_images,c,h,w
|
92 |
+
# for each image, we do self.get_image_features
|
93 |
+
# then we concat the features of all images
|
94 |
+
# pixel_values: (b,num_images,c,h,w) --> (b*num_images,c,h,w)
|
95 |
+
b, num_images, c, h, w = pixel_values.shape
|
96 |
+
pixel_values = pixel_values.view(-1, c, h, w) # (b*num_images,c,h,w)
|
97 |
+
image_embeds = self.get_image_features(
|
98 |
+
pixel_values = pixel_values,
|
99 |
+
vision_feature_layer = vision_feature_layer,
|
100 |
+
vision_feature_select_strategy = vision_feature_select_strategy,
|
101 |
+
)
|
102 |
+
|
103 |
+
# image_features: (b*num_images,seq_len,patch_size) --> (b*num_images*seq_len,patch_size)
|
104 |
+
image_embeds = image_embeds.view(-1,image_embeds.shape[-1])
|
105 |
+
n_image_tokens = (input_ids == self.image_token_idx).sum().item()
|
106 |
+
n_image_features = image_embeds.shape[0]
|
107 |
+
if n_image_tokens != n_image_features:
|
108 |
+
raise ValueError(
|
109 |
+
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
|
110 |
+
)
|
111 |
+
|
112 |
+
mask = input_ids == self.image_token_idx
|
113 |
+
mask_unsqueezed = mask.unsqueeze(-1)
|
114 |
+
mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
|
115 |
+
image_mask = mask_expanded.to(inputs_embeds.device)
|
116 |
+
|
117 |
+
image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
|
118 |
+
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
|
119 |
+
|
120 |
+
|
121 |
+
# change the proprio padding to the real proprio tokens
|
122 |
+
if proprio_projector is not None and proprio is not None:
|
123 |
+
# proprio: (bsz, proprio_dim) or (propro_dim,)
|
124 |
+
proprio = proprio.reshape(batch_size, -1) # (bsz, proprio_dim)
|
125 |
+
proprio_features = proprio_projector(proprio) # (bsz, llm_dim)
|
126 |
+
proprio_features = proprio_features.unsqueeze(dim=1) # (bsz, 1, llm_dim)
|
127 |
+
#(bsz, 1, llm_dim) --> (bsz*1, llm_dim)
|
128 |
+
proprio_features = proprio_features.view(-1, proprio_features.shape[-1])
|
129 |
+
n_proprio_tokens = (input_ids == self.proprio_pad_idx).sum().item()
|
130 |
+
n_proprio_features = proprio_features.shape[0]
|
131 |
+
if n_proprio_tokens != n_proprio_features:
|
132 |
+
raise ValueError(
|
133 |
+
f"Proprio features and proprio tokens do not match: tokens: {n_proprio_tokens}, features {n_proprio_features}"
|
134 |
+
)
|
135 |
+
|
136 |
+
mask = input_ids == self.proprio_pad_idx
|
137 |
+
mask_unsqueezed = mask.unsqueeze(-1)
|
138 |
+
mask_expanded = mask_unsqueezed.expand_as(inputs_embeds)
|
139 |
+
proprio_mask = mask_expanded.to(inputs_embeds.device)
|
140 |
+
|
141 |
+
proprio_features = proprio_features.to(inputs_embeds.device, inputs_embeds.dtype)
|
142 |
+
inputs_embeds = inputs_embeds.masked_scatter(proprio_mask, proprio_features)
|
143 |
+
|
144 |
+
|
145 |
+
# Extract action masks
|
146 |
+
# Action tokens are those in labels that are not ignore, not newline, and not end-of-sequence tokens
|
147 |
+
all_actions_mask = (labels != self.ignore_idx) & (labels != self.stop_index)
|
148 |
+
|
149 |
+
# Replace the embeddings of the action tokens with zeros
|
150 |
+
# (Later on, the positional embeddings will be added to them)
|
151 |
+
all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
|
152 |
+
inputs_embeds = inputs_embeds * ~all_actions_mask
|
153 |
+
outputs = LlavaForConditionalGeneration.forward(
|
154 |
+
self,
|
155 |
+
input_ids = None,
|
156 |
+
attention_mask=attention_mask,
|
157 |
+
position_ids=None,
|
158 |
+
pixel_values=None,
|
159 |
+
labels=labels,
|
160 |
+
inputs_embeds=inputs_embeds,
|
161 |
+
past_key_values=None,
|
162 |
+
use_cache=None,
|
163 |
+
output_attentions=False,
|
164 |
+
output_hidden_states=True,
|
165 |
+
return_dict=True,
|
166 |
+
)
|
167 |
+
# === Otherwise =>> Assume Invalid! ===
|
168 |
+
elif (input_ids.shape[0] != pixel_values.shape[0]) or (inputs_embeds.shape[0] != pixel_values.shape[0]):
|
169 |
+
raise ValueError("Non-homogenous batch of (text, image) input -- forward() does not support mixed batches!")
|
170 |
+
|
171 |
+
else:
|
172 |
+
raise ValueError(
|
173 |
+
"Invalid PrismaticForConditionalGeneration `forward()` call with provided arguments:\n"
|
174 |
+
f"=> `input_ids` = {input_ids is not None}\n"
|
175 |
+
f"=> `attention_mask` = {attention_mask is not None}\n"
|
176 |
+
f"=> `pixel_values` = {pixel_values is not None}\n"
|
177 |
+
f"=> `labels` = {labels is not None}\n"
|
178 |
+
f"=> `input_embeds` = {inputs_embeds is not None}\n"
|
179 |
+
f"=> `past_key_values` = {past_key_values is not None}\n"
|
180 |
+
f"=> `use_cache` = {use_cache}"
|
181 |
+
)
|
182 |
+
|
183 |
+
return outputs
|
184 |
+
|
185 |
+
def _prepare_input_for_action_prediction(self, input_ids, attention_mask):
|
186 |
+
"""Prepares input for action prediction by adding necessary tokens"""
|
187 |
+
# Add (ACTION_DIM * NUM_ACTIONS_CHUNK) placeholder tokens to input_ids to simulate action tokens
|
188 |
+
placeholder_action_token_ids = (
|
189 |
+
torch.ones((input_ids.shape[0], ACTION_DIM * NUM_ACTIONS_CHUNK)).to(input_ids.device).to(input_ids.dtype)
|
190 |
+
)
|
191 |
+
input_ids = torch.cat([input_ids, placeholder_action_token_ids], dim=-1)
|
192 |
+
|
193 |
+
# Add stop token to sequence (needed in non-causal bi-directional self-attention, as it appears at train time)
|
194 |
+
stop_token_id = torch.ones((input_ids.shape[0], 1)).to(input_ids.device).to(input_ids.dtype) * self.stop_index
|
195 |
+
input_ids = torch.cat([input_ids, stop_token_id], dim=-1)
|
196 |
+
|
197 |
+
# Extend the attention mask to fit the new shape of input
|
198 |
+
# Note: Only batch size == 1 supported right now
|
199 |
+
mask_extension = (
|
200 |
+
torch.ones((attention_mask.shape[0], input_ids.shape[-1] - attention_mask.shape[-1]))
|
201 |
+
.to(attention_mask.device)
|
202 |
+
.to(attention_mask.dtype)
|
203 |
+
)
|
204 |
+
attention_mask = torch.cat([attention_mask, mask_extension], dim=-1)
|
205 |
+
|
206 |
+
return input_ids, attention_mask
|
207 |
+
|
208 |
+
def _prepare_labels_for_action_prediction(self, labels, input_ids):
|
209 |
+
"""Creates labels tensor for action prediction if not provided"""
|
210 |
+
# Extend labels tensor with fake action labels
|
211 |
+
ARBITRARY_ACTION_TOKEN_IDX = self.action_token_begin_idx + 1
|
212 |
+
labels_extension = (
|
213 |
+
torch.ones((labels.shape[0], input_ids.shape[-1] - labels.shape[-1])).to(labels.device).to(labels.dtype)
|
214 |
+
* ARBITRARY_ACTION_TOKEN_IDX
|
215 |
+
)
|
216 |
+
labels = torch.cat([labels, labels_extension], dim=-1)
|
217 |
+
|
218 |
+
# Replace last label token with stop token
|
219 |
+
labels[:, -1] = self.stop_index
|
220 |
+
|
221 |
+
return labels
|
222 |
+
|
223 |
+
def _process_action_masks(self, labels):
|
224 |
+
"""Helper to get action masks from labels"""
|
225 |
+
current_action_mask = get_current_action_mask(labels,ignore_index=self.ignore_idx,action_token_begin_idx=self.action_token_begin_idx)
|
226 |
+
next_actions_mask = get_next_actions_mask(labels,ignore_index=self.ignore_idx,action_token_begin_idx=self.action_token_begin_idx)
|
227 |
+
all_actions_mask = current_action_mask | next_actions_mask # (B, seq_len)
|
228 |
+
return all_actions_mask
|
229 |
+
|
230 |
+
def _unnormalize_actions(self, normalized_actions, unnorm_key=None):
|
231 |
+
"""Unnormalize actions using dataset statistics"""
|
232 |
+
action_norm_stats = self.get_action_stats(unnorm_key)
|
233 |
+
|
234 |
+
if ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS:
|
235 |
+
mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["min"], dtype=bool))
|
236 |
+
action_high, action_low = np.array(action_norm_stats["max"]), np.array(action_norm_stats["min"])
|
237 |
+
elif ACTION_PROPRIO_NORMALIZATION_TYPE == NormalizationType.BOUNDS_Q99:
|
238 |
+
mask = action_norm_stats.get("mask", np.ones_like(action_norm_stats["q01"], dtype=bool))
|
239 |
+
action_high, action_low = np.array(action_norm_stats["q99"]), np.array(action_norm_stats["q01"])
|
240 |
+
else:
|
241 |
+
raise ValueError("Unsupported action/proprio normalization type detected!")
|
242 |
+
|
243 |
+
actions = np.where(
|
244 |
+
mask,
|
245 |
+
0.5 * (normalized_actions + 1) * (action_high - action_low + 1e-8) + action_low,
|
246 |
+
normalized_actions,
|
247 |
+
)
|
248 |
+
|
249 |
+
return actions
|
250 |
+
|
251 |
+
def _regression_or_discrete_prediction(
|
252 |
+
self,
|
253 |
+
input_ids,
|
254 |
+
input_embeddings,
|
255 |
+
all_actions_mask,
|
256 |
+
attention_mask,
|
257 |
+
labels,
|
258 |
+
action_head=None,
|
259 |
+
pixel_values = None,
|
260 |
+
):
|
261 |
+
"""Run L1 regression-based continuous action prediction or discrete action tokens prediction."""
|
262 |
+
# Zero out action token embeddings
|
263 |
+
all_actions_mask = all_actions_mask.unsqueeze(-1) # (B, seq_len, 1)
|
264 |
+
input_embeddings = input_embeddings * ~all_actions_mask
|
265 |
+
|
266 |
+
llava_output = LlavaForConditionalGeneration.forward(
|
267 |
+
self,
|
268 |
+
input_ids = None,
|
269 |
+
attention_mask=attention_mask,
|
270 |
+
position_ids=None,
|
271 |
+
pixel_values=None,
|
272 |
+
labels=None,
|
273 |
+
inputs_embeds=input_embeddings,
|
274 |
+
past_key_values=None,
|
275 |
+
use_cache=None,
|
276 |
+
output_attentions=False,
|
277 |
+
output_hidden_states=True,
|
278 |
+
return_dict=True,
|
279 |
+
)
|
280 |
+
all_actions_mask = self._process_action_masks(labels[:,1:])
|
281 |
+
# Extract hidden states for action tokens
|
282 |
+
last_hidden_states = llava_output.hidden_states[-1] # (B, seq_len, D)
|
283 |
+
last_hidden_states = last_hidden_states[:, : -1, :] # (B, act_chunk_len, D)
|
284 |
+
# Use the action mask to extract the hidden states of the actions
|
285 |
+
actions_hidden_states = last_hidden_states[all_actions_mask.squeeze(-1)].unsqueeze(0) # (B, act_chunk_len, D)
|
286 |
+
|
287 |
+
# Handle different prediction methods
|
288 |
+
if action_head is not None:
|
289 |
+
# L1 regression prediction
|
290 |
+
normalized_actions = action_head.predict_action(actions_hidden_states)
|
291 |
+
normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
|
292 |
+
normalized_actions = normalized_actions.float().cpu().detach().numpy()
|
293 |
+
else:
|
294 |
+
# Discrete token-based prediction
|
295 |
+
predicted_action_token_ids = (
|
296 |
+
llava_output.logits[all_actions_mask.squeeze(-1)].unsqueeze(0)
|
297 |
+
.argmax(dim=2)
|
298 |
+
.cpu()
|
299 |
+
.numpy()
|
300 |
+
)
|
301 |
+
# FIXME: We do not support discrete action prediction right now
|
302 |
+
# It seems that vocab_size here is not correct. This should be the dimension of the logit layer, which is actually larger than the vocab_size in the tokenizer. What we actually need here is the vocab_size from the tokenizer.
|
303 |
+
discretized_actions = self.vocab_size - predicted_action_token_ids
|
304 |
+
discretized_actions = np.clip(discretized_actions - 1, a_min=0, a_max=self.bin_centers.shape[0] - 1)
|
305 |
+
normalized_actions = self.bin_centers[discretized_actions]
|
306 |
+
normalized_actions = normalized_actions.reshape(NUM_ACTIONS_CHUNK, ACTION_DIM)
|
307 |
+
|
308 |
+
return normalized_actions, actions_hidden_states
|
309 |
+
|
310 |
+
def predict_action(
|
311 |
+
self,
|
312 |
+
input_ids: Optional[torch.LongTensor] = None,
|
313 |
+
unnorm_key: Optional[str] = None,
|
314 |
+
proprio=None,
|
315 |
+
proprio_projector=None,
|
316 |
+
action_head=None,
|
317 |
+
vision_feature_layer=None,
|
318 |
+
vision_feature_select_strategy=None,
|
319 |
+
**kwargs: str,
|
320 |
+
) -> np.ndarray:
|
321 |
+
"""Predict actions from input sequence, with options for different prediction methods.
|
322 |
+
|
323 |
+
Args:
|
324 |
+
input_ids: Input token ids
|
325 |
+
unnorm_key: Key for unnormalization statistics
|
326 |
+
proprio: Proprioceptive features
|
327 |
+
proprio_projector: Projector for proprioceptive features
|
328 |
+
action_head: Optional head for L1 regression prediction
|
329 |
+
**kwargs: Additional arguments including pixel_values and attention_mask
|
330 |
+
|
331 |
+
Returns:
|
332 |
+
Tuple of (unnormalized_actions, action_hidden_states)
|
333 |
+
"""
|
334 |
+
pixel_values = kwargs["pixel_values"]
|
335 |
+
attention_mask = kwargs["attention_mask"]
|
336 |
+
|
337 |
+
# Create fake labels tensor (needed for action mask)
|
338 |
+
labels = input_ids.clone()
|
339 |
+
labels[:] = self.ignore_idx
|
340 |
+
|
341 |
+
# Prepare inputs by adding necessary tokens
|
342 |
+
input_ids, attention_mask = self._prepare_input_for_action_prediction(input_ids, attention_mask)
|
343 |
+
|
344 |
+
# Update labels tensor for action mask computation later
|
345 |
+
labels = self._prepare_labels_for_action_prediction(labels, input_ids)
|
346 |
+
|
347 |
+
# Get input embeddings and action masks
|
348 |
+
input_embeddings = self.get_input_embeddings()(input_ids)
|
349 |
+
all_actions_mask = self._process_action_masks(labels)
|
350 |
+
|
351 |
+
# vision tokens
|
352 |
+
if pixel_values is not None:
|
353 |
+
vision_feature_layer = (
|
354 |
+
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
|
355 |
+
)
|
356 |
+
vision_feature_select_strategy = (
|
357 |
+
vision_feature_select_strategy
|
358 |
+
if vision_feature_select_strategy is not None
|
359 |
+
else self.config.vision_feature_select_strategy
|
360 |
+
)
|
361 |
+
# pixel_values: b,num_images,c,h,w
|
362 |
+
# for each image, we do self.get_image_features
|
363 |
+
# then we concat the features of all images
|
364 |
+
# pixel_values: (b,num_images,c,h,w) --> (b*num_images,c,h,w)
|
365 |
+
b, num_images, c, h, w = pixel_values.shape
|
366 |
+
pixel_values = pixel_values.view(-1, c, h, w) # (b*num_images,c,h,w)
|
367 |
+
image_embeds = self.get_image_features(
|
368 |
+
pixel_values = pixel_values,
|
369 |
+
vision_feature_layer = vision_feature_layer,
|
370 |
+
vision_feature_select_strategy = vision_feature_select_strategy,
|
371 |
+
)
|
372 |
+
|
373 |
+
# image_features: (b*num_images,seq_len,patch_size) --> (b*num_images*seq_len,patch_size)
|
374 |
+
image_embeds = image_embeds.view(-1,image_embeds.shape[-1])
|
375 |
+
n_image_tokens = (input_ids == self.image_token_idx).sum().item()
|
376 |
+
n_image_features = image_embeds.shape[0]
|
377 |
+
if n_image_tokens != n_image_features:
|
378 |
+
raise ValueError(
|
379 |
+
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
|
380 |
+
)
|
381 |
+
|
382 |
+
mask = input_ids == self.image_token_idx
|
383 |
+
mask_unsqueezed = mask.unsqueeze(-1)
|
384 |
+
mask_expanded = mask_unsqueezed.expand_as(input_embeddings)
|
385 |
+
image_mask = mask_expanded.to(input_embeddings.device)
|
386 |
+
|
387 |
+
image_embeds = image_embeds.to(input_embeddings.device, input_embeddings.dtype)
|
388 |
+
input_embeddings = input_embeddings.masked_scatter(image_mask, image_embeds)
|
389 |
+
|
390 |
+
# Add proprioceptive features if provided
|
391 |
+
use_proprio = proprio_projector is not None and proprio is not None
|
392 |
+
if use_proprio:
|
393 |
+
batch_size = input_ids.shape[0] if input_ids is not None else input_embeddings.shape[0] # type: ignore
|
394 |
+
proprio = torch.Tensor(proprio).to(input_embeddings.device, dtype=input_embeddings.dtype)
|
395 |
+
if proprio_projector is not None and proprio is not None:
|
396 |
+
# proprio: (bsz, proprio_dim) or (propro_dim,)
|
397 |
+
proprio = proprio.reshape(batch_size, -1) # (bsz, proprio_dim)
|
398 |
+
proprio_features = proprio_projector(proprio) # (bsz, llm_dim)
|
399 |
+
proprio_features = proprio_features.unsqueeze(dim=1) # (bsz, 1, llm_dim)
|
400 |
+
#(bsz, 1, llm_dim) --> (bsz*1, llm_dim)
|
401 |
+
proprio_features = proprio_features.view(-1, proprio_features.shape[-1])
|
402 |
+
n_proprio_tokens = (input_ids == self.proprio_pad_idx).sum().item()
|
403 |
+
n_proprio_features = proprio_features.shape[0]
|
404 |
+
if n_proprio_tokens != n_proprio_features:
|
405 |
+
raise ValueError(
|
406 |
+
f"Proprio features and proprio tokens do not match: tokens: {n_proprio_tokens}, features {n_proprio_features}"
|
407 |
+
)
|
408 |
+
|
409 |
+
mask = input_ids == self.proprio_pad_idx
|
410 |
+
mask_unsqueezed = mask.unsqueeze(-1)
|
411 |
+
mask_expanded = mask_unsqueezed.expand_as(input_embeddings)
|
412 |
+
proprio_mask = mask_expanded.to(input_embeddings.device)
|
413 |
+
|
414 |
+
proprio_features = proprio_features.to(input_embeddings.device, input_embeddings.dtype)
|
415 |
+
input_embeddings = input_embeddings.masked_scatter(proprio_mask, proprio_features)
|
416 |
+
|
417 |
+
# Run regression or discrete token-based prediction
|
418 |
+
normalized_actions, actions_hidden_states = self._regression_or_discrete_prediction(
|
419 |
+
input_ids,
|
420 |
+
input_embeddings,
|
421 |
+
all_actions_mask,
|
422 |
+
attention_mask,
|
423 |
+
labels,
|
424 |
+
action_head,
|
425 |
+
pixel_values,
|
426 |
+
)
|
427 |
+
|
428 |
+
# Unnormalize predicted actions
|
429 |
+
actions = self._unnormalize_actions(normalized_actions, unnorm_key)
|
430 |
+
|
431 |
+
return actions, actions_hidden_states
|
432 |
+
|
433 |
+
@staticmethod
|
434 |
+
def _check_unnorm_key(norm_stats: Dict[str, Dict[str, Any]], unnorm_key: Optional[str]) -> str:
|
435 |
+
"""Validate and resolve the unnormalization key for action statistics"""
|
436 |
+
if unnorm_key is None:
|
437 |
+
assert len(norm_stats) == 1, (
|
438 |
+
f"Your model was trained on more than one dataset, "
|
439 |
+
f"please pass a `unnorm_key` from the following options to choose the statistics "
|
440 |
+
f"used for un-normalizing actions: {norm_stats.keys()}"
|
441 |
+
)
|
442 |
+
unnorm_key = next(iter(norm_stats.keys()))
|
443 |
+
|
444 |
+
assert unnorm_key in norm_stats, (
|
445 |
+
f"The `unnorm_key` you chose is not in the set of available dataset statistics, "
|
446 |
+
f"please choose from: {norm_stats.keys()}"
|
447 |
+
)
|
448 |
+
return unnorm_key
|
449 |
+
|
450 |
+
def get_action_dim(self, unnorm_key: Optional[str] = None) -> int:
|
451 |
+
"""Get the dimensionality of the policy's action space."""
|
452 |
+
unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
|
453 |
+
return len(self.norm_stats[unnorm_key]["action"]["min"])
|
454 |
+
|
455 |
+
def get_action_stats(self, unnorm_key: Optional[str] = None) -> Dict[str, Any]:
|
456 |
+
"""Get all the logged statistics for the given dataset."""
|
457 |
+
unnorm_key = self._check_unnorm_key(self.norm_stats, unnorm_key)
|
458 |
+
return self.norm_stats[unnorm_key]["action"]
|
459 |
+
|
config.json
ADDED
@@ -0,0 +1,70 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"Llava_OpenVLAForActionPrediction"
|
4 |
+
],
|
5 |
+
"auto_map": {
|
6 |
+
"AutoConfig": "configuration_bit_vla.Bitvla_Config",
|
7 |
+
"AutoModelForVision2Seq": "bitvla_for_action_prediction.BitVLAForActionPrediction"
|
8 |
+
},
|
9 |
+
"image_seq_length": 256,
|
10 |
+
"image_token_index": 128260,
|
11 |
+
"model_type": "openvla",
|
12 |
+
"multimodal_projector_bias": true,
|
13 |
+
"n_action_bins": 256,
|
14 |
+
"norm_stats": null,
|
15 |
+
"projector_hidden_act": "gelu",
|
16 |
+
"text_config": {
|
17 |
+
"_attn_implementation_autoset": true,
|
18 |
+
"_name_or_path": "/hongyu/bitvla_object/",
|
19 |
+
"architectures": [
|
20 |
+
"BitNetForCausalLM"
|
21 |
+
],
|
22 |
+
"attention_bias": false,
|
23 |
+
"attention_dropout": 0.0,
|
24 |
+
"auto_map": {
|
25 |
+
"AutoConfig": "configuration_bitnet.BitNetConfig",
|
26 |
+
"AutoModelForCausalLM": "modeling_bitnet.BitNetForCausalLM"
|
27 |
+
},
|
28 |
+
"hidden_act": "silu",
|
29 |
+
"hidden_size": 2560,
|
30 |
+
"initializer_range": 0.02,
|
31 |
+
"intermediate_size": 6912,
|
32 |
+
"max_position_embeddings": 4096,
|
33 |
+
"max_window_layers": 28,
|
34 |
+
"model_path": "/hongyu/bitvla_object/",
|
35 |
+
"model_type": "BitNet",
|
36 |
+
"num_attention_heads": 20,
|
37 |
+
"num_hidden_layers": 30,
|
38 |
+
"num_key_value_heads": 5,
|
39 |
+
"rms_norm_eps": 1e-05,
|
40 |
+
"rope_theta": 500000.0,
|
41 |
+
"sliding_window": 4096,
|
42 |
+
"torch_dtype": "bfloat16",
|
43 |
+
"use_cache": true,
|
44 |
+
"use_sliding_window": false,
|
45 |
+
"vocab_size": 128264
|
46 |
+
},
|
47 |
+
"torch_dtype": "bfloat16",
|
48 |
+
"transformers_version": "4.51.0.dev0",
|
49 |
+
"vision_config": {
|
50 |
+
"_attn_implementation_autoset": true,
|
51 |
+
"attention_dropout": 0.0,
|
52 |
+
"hidden_act": "gelu_pytorch_tanh",
|
53 |
+
"hidden_size": 1152,
|
54 |
+
"image_size": 224,
|
55 |
+
"intermediate_size": 4304,
|
56 |
+
"layer_norm_eps": 1e-06,
|
57 |
+
"model_type": "siglip_vision_model",
|
58 |
+
"num_attention_heads": 16,
|
59 |
+
"num_channels": 3,
|
60 |
+
"num_hidden_layers": 26,
|
61 |
+
"patch_size": 14,
|
62 |
+
"torch_dtype": "bfloat16",
|
63 |
+
"vision_use_head": false,
|
64 |
+
"vit_act_bits": 8,
|
65 |
+
"vit_weight_bits": 1
|
66 |
+
},
|
67 |
+
"vision_feature_layer": -1,
|
68 |
+
"vision_feature_select_strategy": "full",
|
69 |
+
"vocab_size": 128264
|
70 |
+
}
|
configuration_bit_vla.py
ADDED
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from transformers import LlavaConfig
|
2 |
+
from typing import Dict, List, Optional
|
3 |
+
|
4 |
+
class Bitvla_Config(LlavaConfig):
|
5 |
+
model_type: str = "bitvla"
|
6 |
+
|
7 |
+
def __init__(
|
8 |
+
self,
|
9 |
+
norm_stats: Optional[Dict[str, Dict[str, Dict[str, Dict[str, List[float]]]]]] = None,
|
10 |
+
n_action_bins: int = 256,
|
11 |
+
**kwargs: str,
|
12 |
+
) -> None:
|
13 |
+
self.norm_stats, self.n_action_bins = norm_stats, n_action_bins
|
14 |
+
super().__init__(**kwargs)
|
dataset_statistics.json
ADDED
@@ -0,0 +1,133 @@
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"libero_object_no_noops": {
|
3 |
+
"action": {
|
4 |
+
"mean": [
|
5 |
+
0.07096529006958008,
|
6 |
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0.13498851656913757,
|
7 |
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|
8 |
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0.00123520044144243,
|
9 |
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0.006998839322477579,
|
10 |
+
-0.015027612447738647,
|
11 |
+
0.46428999304771423
|
12 |
+
],
|
13 |
+
"std": [
|
14 |
+
0.2681235373020172,
|
15 |
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0.43846824765205383,
|
16 |
+
0.4474974274635315,
|
17 |
+
0.024446550756692886,
|
18 |
+
0.049355510622262955,
|
19 |
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0.042107198387384415,
|
20 |
+
0.49879148602485657
|
21 |
+
],
|
22 |
+
"max": [
|
23 |
+
0.9375,
|
24 |
+
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|
25 |
+
0.9375,
|
26 |
+
0.17678570747375488,
|
27 |
+
0.35035714507102966,
|
28 |
+
0.1810714304447174,
|
29 |
+
1.0
|
30 |
+
],
|
31 |
+
"min": [
|
32 |
+
-0.8839285969734192,
|
33 |
+
-0.9375,
|
34 |
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-0.9375,
|
35 |
+
-0.15000000596046448,
|
36 |
+
-0.29035714268684387,
|
37 |
+
-0.32892856001853943,
|
38 |
+
0.0
|
39 |
+
],
|
40 |
+
"q01": [
|
41 |
+
-0.5383928418159485,
|
42 |
+
-0.8758928775787354,
|
43 |
+
-0.9375,
|
44 |
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-0.06964285671710968,
|
45 |
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-0.11678571254014969,
|
46 |
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-0.15964286029338837,
|
47 |
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0.0
|
48 |
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],
|
49 |
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"q99": [
|
50 |
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|
51 |
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0.84375,
|
52 |
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|
53 |
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|
54 |
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0.14892856776714325,
|
55 |
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0.0867857113480568,
|
56 |
+
1.0
|
57 |
+
],
|
58 |
+
"mask": [
|
59 |
+
true,
|
60 |
+
true,
|
61 |
+
true,
|
62 |
+
true,
|
63 |
+
true,
|
64 |
+
true,
|
65 |
+
false
|
66 |
+
]
|
67 |
+
},
|
68 |
+
"proprio": {
|
69 |
+
"mean": [
|
70 |
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|
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|
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|
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|
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|
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|
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|
77 |
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-0.030556727200746536
|
78 |
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],
|
79 |
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"std": [
|
80 |
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|
81 |
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|
82 |
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|
83 |
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0.0868484303355217,
|
84 |
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|
85 |
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|
86 |
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0.00956575945019722,
|
87 |
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0.009197483770549297
|
88 |
+
],
|
89 |
+
"max": [
|
90 |
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|
91 |
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|
92 |
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0.3857804834842682,
|
93 |
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3.4003844261169434,
|
94 |
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0.7954911589622498,
|
95 |
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0.6642207503318787,
|
96 |
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0.04104341194033623,
|
97 |
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-0.00018117300351150334
|
98 |
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],
|
99 |
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"min": [
|
100 |
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|
101 |
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-0.29457300901412964,
|
102 |
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|
103 |
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|
104 |
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|
105 |
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|
106 |
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|
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|
108 |
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],
|
109 |
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"q01": [
|
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|
111 |
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|
112 |
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|
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|
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|
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|
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|
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],
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"q99": [
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|
121 |
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|
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|
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|
127 |
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|
128 |
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]
|
129 |
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},
|
130 |
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"num_transitions": 66984,
|
131 |
+
"num_trajectories": 454
|
132 |
+
}
|
133 |
+
}
|
generation_config.json
ADDED
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"bos_token_id": 128000,
|
4 |
+
"eos_token_id": 128001,
|
5 |
+
"transformers_version": "4.51.0.dev0"
|
6 |
+
}
|
model-00001-of-00002.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:274ec5ab754d09408277c2eab70a9f6b40622c77ab93203c4ee8d1f22c46c3b3
|
3 |
+
size 4977134296
|
model-00002-of-00002.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:30959e5e8cc1b395aeb8d8fb690d8a07a9e6a3ad413ae09f2b6573890a3d8924
|
3 |
+
size 662066096
|
model.safetensors.index.json
ADDED
@@ -0,0 +1,762 @@
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|
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|
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proprio_projector--10000_checkpoint.pt
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"lstrip": false,
|
1778 |
+
"normalized": false,
|
1779 |
+
"rstrip": false,
|
1780 |
+
"single_word": false
|
1781 |
+
},
|
1782 |
+
"pad_token": "<|pad|>"
|
1783 |
+
}
|
tokenizer.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2241223512a94b8bd5b6bd96437088e6473e1037a1ddcdc88f2cb7896e75d91f
|
3 |
+
size 17207016
|
tokenizer_config.json
ADDED
@@ -0,0 +1,2383 @@
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|
1 |
+
{
|
2 |
+
"added_tokens_decoder": {
|
3 |
+
"128000": {
|
4 |
+
"content": "<|begin_of_text|>",
|
5 |
+
"lstrip": false,
|
6 |
+
"normalized": false,
|
7 |
+
"rstrip": false,
|
8 |
+
"single_word": false,
|
9 |
+
"special": true
|
10 |
+
},
|
11 |
+
"128001": {
|
12 |
+
"content": "<|end_of_text|>",
|
13 |
+
"lstrip": false,
|
14 |
+
"normalized": false,
|
15 |
+
"rstrip": false,
|
16 |
+
"single_word": false,
|
17 |
+
"special": true
|
18 |
+
},
|
19 |
+
"128002": {
|
20 |
+
"content": "<|pad|>",
|
21 |
+
"lstrip": false,
|
22 |
+
"normalized": false,
|
23 |
+
"rstrip": false,
|
24 |
+
"single_word": false,
|
25 |
+
"special": true
|
26 |
+
},
|
27 |
+
"128003": {
|
28 |
+
"content": "<|reserved_special_token_1|>",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false,
|
33 |
+
"special": true
|
34 |
+
},
|
35 |
+
"128004": {
|
36 |
+
"content": "<|reserved_special_token_2|>",
|
37 |
+
"lstrip": false,
|
38 |
+
"normalized": false,
|
39 |
+
"rstrip": false,
|
40 |
+
"single_word": false,
|
41 |
+
"special": true
|
42 |
+
},
|
43 |
+
"128005": {
|
44 |
+
"content": "<|reserved_special_token_3|>",
|
45 |
+
"lstrip": false,
|
46 |
+
"normalized": false,
|
47 |
+
"rstrip": false,
|
48 |
+
"single_word": false,
|
49 |
+
"special": true
|
50 |
+
},
|
51 |
+
"128006": {
|
52 |
+
"content": "<|start_header_id|>",
|
53 |
+
"lstrip": false,
|
54 |
+
"normalized": false,
|
55 |
+
"rstrip": false,
|
56 |
+
"single_word": false,
|
57 |
+
"special": true
|
58 |
+
},
|
59 |
+
"128007": {
|
60 |
+
"content": "<|end_header_id|>",
|
61 |
+
"lstrip": false,
|
62 |
+
"normalized": false,
|
63 |
+
"rstrip": false,
|
64 |
+
"single_word": false,
|
65 |
+
"special": true
|
66 |
+
},
|
67 |
+
"128008": {
|
68 |
+
"content": "<|reserved_special_token_4|>",
|
69 |
+
"lstrip": false,
|
70 |
+
"normalized": false,
|
71 |
+
"rstrip": false,
|
72 |
+
"single_word": false,
|
73 |
+
"special": true
|
74 |
+
},
|
75 |
+
"128009": {
|
76 |
+
"content": "<|eot_id|>",
|
77 |
+
"lstrip": false,
|
78 |
+
"normalized": false,
|
79 |
+
"rstrip": false,
|
80 |
+
"single_word": false,
|
81 |
+
"special": true
|
82 |
+
},
|
83 |
+
"128010": {
|
84 |
+
"content": "<|image_pad|>",
|
85 |
+
"lstrip": false,
|
86 |
+
"normalized": false,
|
87 |
+
"rstrip": false,
|
88 |
+
"single_word": false,
|
89 |
+
"special": true
|
90 |
+
},
|
91 |
+
"128011": {
|
92 |
+
"content": "<proprio_pad>",
|
93 |
+
"lstrip": false,
|
94 |
+
"normalized": false,
|
95 |
+
"rstrip": false,
|
96 |
+
"single_word": false,
|
97 |
+
"special": true
|
98 |
+
},
|
99 |
+
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"<action_243>",
|
2361 |
+
"<action_244>",
|
2362 |
+
"<action_245>",
|
2363 |
+
"<action_246>",
|
2364 |
+
"<action_247>",
|
2365 |
+
"<action_248>",
|
2366 |
+
"<action_249>",
|
2367 |
+
"<action_250>",
|
2368 |
+
"<action_251>"
|
2369 |
+
],
|
2370 |
+
"bos_token": "<|begin_of_text|>",
|
2371 |
+
"chat_template": "System: A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.<|eot_id|>{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = message['role'] | capitalize + ': '+ message['content'] | trim + '<|eot_id|>' %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant: ' }}{% endif %}",
|
2372 |
+
"clean_up_tokenization_spaces": true,
|
2373 |
+
"eos_token": "<|end_of_text|>",
|
2374 |
+
"extra_special_tokens": {},
|
2375 |
+
"model_input_names": [
|
2376 |
+
"input_ids",
|
2377 |
+
"attention_mask"
|
2378 |
+
],
|
2379 |
+
"model_max_length": 1000000000000000019884624838656,
|
2380 |
+
"pad_token": "<|pad|>",
|
2381 |
+
"processor_class": "LlavaProcessor",
|
2382 |
+
"tokenizer_class": "PreTrainedTokenizer"
|
2383 |
+
}
|